In this notebook we will add TP53 alteration status as discussed in #837:

TP53 altered - loss, if :

Note: CNV and SV will be considered as the same event, but we will be adding SV_counts and SV.type to the altered status output file

TP53 altered - activated, if:

Setup

library("ggpubr")
Loading required package: ggplot2
Registered S3 methods overwritten by 'tibble':
  method     from  
  format.tbl pillar
  print.tbl  pillar
Loading required package: magrittr
library("ggthemes")
library("tidyverse")
── Attaching packages ────────────────────────────────── tidyverse 1.2.1 ──
✔ tibble  2.1.3     ✔ purrr   0.3.2
✔ tidyr   0.8.3     ✔ dplyr   1.0.7
✔ readr   1.3.1     ✔ stringr 1.4.0
✔ tibble  2.1.3     ✔ forcats 0.4.0
── Conflicts ───────────────────────────────────── tidyverse_conflicts() ──
✖ tidyr::extract()   masks magrittr::extract()
✖ dplyr::filter()    masks stats::filter()
✖ dplyr::lag()       masks stats::lag()
✖ purrr::set_names() masks magrittr::set_names()
library("ggfortify")
library("broom")

# rootdir
root_dir <- rprojroot::find_root(rprojroot::has_dir(".git"))
data_dir <- file.path(root_dir, "data")

input_dir <- file.path(root_dir,
                         "analyses",
                         "tp53_nf1_score",
                         "input")
# cell composition
cell_line_composition <- read_tsv(file.path("..",
                                            "molecular-subtyping-HGG",
                                            "input",
                                            "cell-line-composition.tsv"),
                                  col_types = 
                                    readr::cols( aliquot_id  = readr::col_character())) 

# histology
if ( params$base_run ==0 ){
  clinical<-read.delim(file.path(data_dir,"histologies.tsv"), stringsAsFactors = FALSE)
} else{
  clinical<-read.delim(file.path(data_dir,"histologies-base.tsv"), stringsAsFactors = FALSE)  
}

histology <- clinical %>%
   select("Kids_First_Biospecimen_ID",
          "sample_id",
          "cancer_predispositions",
          "sample_type",
          "experimental_strategy",
          "composition") %>%
   # merge cell line composition
  left_join(cell_line_composition)
Joining, by = c("Kids_First_Biospecimen_ID", "sample_id")
# Cancer database
hotspot_database_2017_tp53_snv <- readxl::read_xls(file.path(input_dir,"hotspots_v2.xls"),sheet = 1) %>%
  filter(Hugo_Symbol == "TP53")
hotspot_database_2017_tp53_indel <- readxl::read_xls(file.path(input_dir,"hotspots_v2.xls"),sheet = 2) %>%
  filter(Hugo_Symbol == "TP53")

# p53, p63 and p73 functional sites
functional_sites_tp53 <- read_tsv(file.path(input_dir,"hotspot_Chen_2006.tsv")) 
Parsed with column specification:
cols(
  p53 = col_character(),
  p63 = col_character(),
  p73 = col_character()
)
results_dir <- file.path(root_dir,
                         "analyses",
                         "tp53_nf1_score",
                         "results")

if (!dir.exists(results_dir)) {
  dir.create(results_dir)
}

# classifier scores
classifier_score <- read_tsv(
  file.path(
    results_dir,
    "gene-expression-rsem-tpm-collapsed_classifier_scores.tsv"))%>%
  select(sample_id,tp53_score) %>%
  dplyr::rename(Kids_First_Biospecimen_ID_RNA=sample_id) %>%
  left_join(histology ,by=c("Kids_First_Biospecimen_ID_RNA"="Kids_First_Biospecimen_ID")) 
Parsed with column specification:
cols(
  sample_id = col_character(),
  ras_score = col_double(),
  tp53_score = col_double(),
  nf1_score = col_double(),
  ras_shuffle = col_double(),
  tp53_shuffle = col_double(),
  nf1_shuffle = col_double()
)
# Copy number per sample 
# overlapping functional domain
cnv_domain_overlap <- read_tsv(
  file.path(
    results_dir,
    "loss_overlap_domains_tp53.tsv"))
Parsed with column specification:
cols(
  biospecimen_id = col_character(),
  copy_number = col_double(),
  ploidy = col_double(),
  domain = col_character(),
  sample_id = col_character()
)
# Structural variant overlapping 
# or within gene locus of TP53
sv_overlap <- read_tsv(
  file.path(
    results_dir,
    "sv_overlap_tp53.tsv"))
Parsed with column specification:
cols(
  SV.chrom = col_double(),
  SV.start = col_double(),
  SV.end = col_double(),
  SV.length = col_double(),
  Kids.First.Biospecimen.ID.Tumor = col_character(),
  SV.type = col_character(),
  Gene.name = col_character(),
  ALT = col_character(),
  sample_id = col_character()
)
# Structural variant as a fusion in exon1/intron1
fusion_overlap <- read_tsv(
  file.path(
    results_dir,
    "fusion_bk_tp53_loss.tsv"))
Parsed with column specification:
cols(
  seqnames = col_character(),
  start = col_double(),
  end = col_double(),
  width = col_double(),
  strand = col_character(),
  Sample = col_character(),
  sample_id = col_character(),
  FusionName = col_character(),
  variable = col_character(),
  value = col_time(format = "")
)
Warning: 73 parsing failures.
row   col   expected     actual                                                                                                   file
  1 value valid date 17:7687377 '/home/rstudio/OpenPedCan/OpenPedCan-analysis/analyses/tp53_nf1_score/results/fusion_bk_tp53_loss.tsv'
  2 value valid date 17:7687377 '/home/rstudio/OpenPedCan/OpenPedCan-analysis/analyses/tp53_nf1_score/results/fusion_bk_tp53_loss.tsv'
  3 value valid date 17:7687377 '/home/rstudio/OpenPedCan/OpenPedCan-analysis/analyses/tp53_nf1_score/results/fusion_bk_tp53_loss.tsv'
  4 value valid date 17:7687377 '/home/rstudio/OpenPedCan/OpenPedCan-analysis/analyses/tp53_nf1_score/results/fusion_bk_tp53_loss.tsv'
  5 value valid date 17:7687377 '/home/rstudio/OpenPedCan/OpenPedCan-analysis/analyses/tp53_nf1_score/results/fusion_bk_tp53_loss.tsv'
... ..... .......... .......... ......................................................................................................
See problems(...) for more details.

Check if all function sites are in hotspots

All functional sites are just 1 base so checking in hotspot_database_2017_snv

functional_sites_tp53 %>%
  filter(!gsub("[A-Z|a-z]","",p53) %in% hotspot_database_2017_tp53_snv$Amino_Acid_Position )

2 functional sites are missing in hotspots database.

SNV for TP53

Removing Silent or Intron classified variants to capture only putative damaging mutations

consensus_tp53_snv_indel <- data.table::fread(
  file.path(data_dir,"snv-consensus-plus-hotspots.maf.tsv.gz"),
                                   select = c("Chromosome",
                                              "Start_Position",
                                              "End_Position",
                                              "Strand",
                                              "Variant_Classification",
                                              "Tumor_Sample_Barcode",
                                              "Hugo_Symbol",
                                              "HGVSp_Short"),
                                   data.table = FALSE) %>%
  filter(Hugo_Symbol == "TP53") %>%
  filter(!(Variant_Classification %in% c("Silent", "Intron",
                                         # remove other non-amino acid SNVs
                                         "3'Flank" ,"5'Flank",
                                         "3'UTR", "5'UTR"  )))
Registered S3 method overwritten by 'R.oo':
  method        from       
  throw.default R.methodsS3

Gather annotation for SNV

hotspots : overlaps AA position which are statistically significant SNV/Indels activating : overlaps AA position which are found to act as gain-of-function according to literature

consensus_tp53_snv_indel <- consensus_tp53_snv_indel %>%
  mutate(
    hotspot = case_when(
      # strip REF and Variant AA to get Amino_Acid_Position in consensus maf
      (gsub("[A-Z|a-z]|[.]","",HGVSp_Short) %in% 
         # if overlaps the hotspot Amino_Acid_Position 
         hotspot_database_2017_tp53_snv$Amino_Acid_Position)  ~ 1,
      TRUE ~ 0),
    activating = case_when(
      # strip REF and Variant AA to get Amino_Acid_Position in consensus maf
      (gsub("[A-Z|a-z]|[.]","",HGVSp_Short) %in% 
         # if overlaps the activating  Amino_Acid_Position 
         c("273","248"))  ~ 1,
      TRUE ~ 0
    )
  )

Gather SNV,CNV, classifier and cancer_predisposition values

We will gather values per sample_id since DNA and RNA samples can be only matched by sample_id

tp53_alterations <- histology %>%
  filter(experimental_strategy != "RNA-Seq") %>%
  # merge cell line composition
  left_join(cell_line_composition) %>%
  # filter for Tumors
  filter(sample_type=="Tumor") %>%
  # join consensus calls overlapping hotspots
  left_join(consensus_tp53_snv_indel, 
            by=c("Kids_First_Biospecimen_ID"="Tumor_Sample_Barcode")) %>%
  # join filtered cnv losses
  left_join(cnv_domain_overlap,by=c("Kids_First_Biospecimen_ID"="biospecimen_id",
                                    "sample_id")) %>%
  # join SV 
  left_join(sv_overlap,by=c("Kids_First_Biospecimen_ID"="Kids.First.Biospecimen.ID.Tumor",
                            "sample_id")) %>%
  dplyr::rename(Kids_First_Biospecimen_ID_DNA = Kids_First_Biospecimen_ID) %>%
  # join fusion
  left_join(fusion_overlap,by="sample_id") 
Joining, by = c("Kids_First_Biospecimen_ID", "sample_id", "Kids_First_Participant_ID", "cell_line_composition", "aliquot_id")
tp53_alterations_score <- tp53_alterations %>%
  # add classifier score
  full_join(classifier_score,by=c("sample_id",
                                  "cell_line_composition",
                                  "cancer_predispositions",
                                  "sample_type",
                                  "Kids_First_Participant_ID")) %>%
  # select useful columns
  select(sample_id,Kids_First_Biospecimen_ID_RNA,Kids_First_Biospecimen_ID_DNA,
         tp53_score,cancer_predispositions,HGVSp_Short,copy_number,SV.type,FusionName,hotspot,activating) %>%
  arrange(sample_id) %>%
  unique() %>%
  replace_na(list(hotspot = 0, activating = 0))

tp53_alterations_score

Convert the above to wide format

tp53_alterations_score_wide <- tp53_alterations_score %>%
  # group 
  group_by(sample_id,
           Kids_First_Biospecimen_ID_DNA,
           Kids_First_Biospecimen_ID_RNA,
           cancer_predispositions,
           tp53_score) %>%
  # 
  # sample_id as rows add SNV counts,CNV loss counts 
  # and hotspot/activating mutation annotation  
  summarise(
    # summarize length of SNV and CNV alterations 
   SNV_indel_counts= length(unique(HGVSp_Short[!is.na(HGVSp_Short)])), 
    CNV_loss_counts = length(unique(copy_number[!is.na(copy_number)])),
    SV_counts = length(unique(SV.type[!is.na(SV.type)])),
    Fusion_counts = length(unique(FusionName[!is.na(FusionName)])),
    HGVSp_Short = toString(unique(HGVSp_Short)),
    CNV_loss_evidence = toString(unique(copy_number)),
    SV_type = toString(unique(SV.type)),
    Fusion_evidence = toString(unique(FusionName)),
    # summarize unique hotspot values per sample_id
    hotspot = max(unique(hotspot[!is.na(hotspot) ])),
    activating = max(unique(activating[!is.na(activating)]))) 
`summarise()` has grouped output by 'sample_id', 'Kids_First_Biospecimen_ID_DNA', 'Kids_First_Biospecimen_ID_RNA', 'cancer_predispositions'. You can override using the `.groups` argument.
tp53_alterations_score_wide

Add annotation

As discussed above we want to annotate TP53 mutants that are putative loss-of-function OR gain-of-function.

tp53_alterations_score_wide <- tp53_alterations_score_wide %>%
  mutate(
    # add tp53_altered annotation column
    tp53_altered = 
      case_when(
        # when activating == 0
        activating == 0 &
          # check if mutated variant AA position overlaps hotspot database 
          ( hotspot == 1  |
              # check if sample_id has SNV+(CNV|SV) mutation
              # suggesting both alleles are mutated
              (SNV_indel_counts >= 1 & 
                 (CNV_loss_counts >=1 | SV_counts >=1) ) |
              # check if more than 1 SNV is present
              # suggesting both alleles are mutated
              SNV_indel_counts > 1 |
              # check if SNV mutant and has
              # Li-Fraumeni syndrome suggesting
              # germline TP53 mutant as cancer predisposition 
              (SNV_indel_counts >= 1 & 
                             grepl("Li-Fraumeni syndrome",cancer_predispositions)) |
              # check if CNV|SV mutant  and has
              # Li-Fraumeni syndrome suggesting
              # germline TP53 mutant as cancer predisposition 
              ((CNV_loss_counts >= 1 | SV_counts >=1 ) & 
                             grepl("Li-Fraumeni syndrome",cancer_predispositions)) |
              # check if tp53 inactivating score for RNA_Seq is greater that 0.5
              # and has Li-Fraumeni syndrome suggesting
              # germline TP53 mutant as cancer predisposition 
              (tp53_score > 0.5 & 
                             grepl("Li-Fraumeni syndrome",cancer_predispositions)) |
              # check if tp53 inactivating score for RNA_Seq is greater that 0.5
              # and has 1 or more SNV| (CNV|SV) loss in TP53
              (tp53_score > 0.5 & 
                             (SNV_indel_counts >= 1 | (CNV_loss_counts >=1 | SV_counts >=1 )))
          ) ~ "loss",
        # when activating == 1
        activating == 1 ~ "activated",
        # if no evidence supports TP53 inativation or activation 
        TRUE ~ "Other"
      )
  )

Explore distribution of tp53_altered status vs tp53 inactivation scores

ggplot(tp53_alterations_score_wide, aes(x = factor(tp53_altered), y = tp53_score)) +
  geom_violin()+
  geom_jitter(alpha = 0.5, width = 0.2) +
  stat_compare_means() +
  theme_bw() +
  ggtitle("Distribution of scores across tp53 altered status") +
  theme(axis.text.x = element_text(angle = 60, hjust = 1))+
  xlab("tp53 altered status") 
Warning: Removed 4571 rows containing non-finite values (stat_ydensity).
Warning: Removed 4571 rows containing non-finite values
(stat_compare_means).
Warning: `select_()` was deprecated in dplyr 0.7.0.
Please use `select()` instead.
Warning: `tbl_df()` was deprecated in dplyr 1.0.0.
Please use `tibble::as_tibble()` instead.
Warning: Removed 4571 rows containing missing values (geom_point).

TP53 mutants with gain-of-function (activating) mutants promote tumorigenesis according to literature. Reference and reference have similar distribution of classifier scores to that of potential loss-of-function (multi-allelic) Tp53 mutations.

“Other” annotated samples either don’t have any high-confidence loss/gain TP53 SNVs nor CNV losses OR DNA sample is not available.

Expression profile for activating vs loss TP53 status

expr_combined <- readRDS(file.path(data_dir,"gene-counts-rsem-expected_count-collapsed.rds"))

Expression profile for activating vs loss TP53 status

# iterate through all possible RNA library type 
# first filter to 
clinical_filtered <- clinical %>% 
  filter(sample_type == "Tumor",
         experimental_strategy == "RNA-Seq") %>%
  filter(cohort =="PBTA") %>%
  filter(!is.na(RNA_library))

# generate list of all RNA library type
rna_library_list <- clinical_filtered %>% 
  pull(RNA_library) %>% 
  unique()

for(i in 1:length(rna_library_list)) {
  
  library_each <- rna_library_list[[i]]
  # pull BS IDs for that RNA library type
  library_samples <- clinical_filtered %>% 
    filter(RNA_library == library_each) %>%
    pull(Kids_First_Biospecimen_ID) %>% 
    unique()
  # subset expression matrix using that
  expr_each <- expr_combined %>% select(library_samples)

  # subset to TP53
  subset_expr_each <- t(expr_each)[,"TP53"] %>% 
    as.data.frame() 
  colnames(subset_expr_each) <- "TP53"
  
  # combine the TP53 alteration information
  subset_expr_combined <- subset_expr_each %>%
    tibble::rownames_to_column() %>% 
    left_join(tp53_alterations_score_wide,by=c("rowname"="Kids_First_Biospecimen_ID_RNA")) %>%
    filter(tp53_altered %in% c("activated","loss"))
  
  # plot distribution of TP53 gene expression 
  plot<- ggplot(subset_expr_combined, aes(x = factor(tp53_altered), y = TP53)) +
    geom_violin()+
    geom_jitter(alpha = 0.5, width = 0.2) +
    stat_compare_means() +
    theme_bw() +
    ggtitle("Distribution of TP53 expression across tp53 altered status") +
    theme(axis.text.x = element_text(angle = 60, hjust = 1))+
    xlab(paste0("tp53 altered status wth RNA library ", library_each))

  print(plot)
}
Note: Using an external vector in selections is ambiguous.
ℹ Use `all_of(library_samples)` instead of `library_samples` to silence this message.
ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
This message is displayed once per session.

Check if other cancer predisposition have high TP53 inactivation scores

ggplot(tp53_alterations_score_wide, aes(x = factor(tp53_altered), y = tp53_score)) +
  geom_violin()+
  geom_jitter(alpha = 0.5, width = 0.2) +
  theme_bw() +
  ggtitle("Distribution of scores across tp53 altered status") +
  theme(axis.text.x = element_text(angle = 60, hjust = 1))+
  xlab("tp53 altered status") +
  facet_wrap(.~cancer_predispositions)
Warning: Removed 4571 rows containing non-finite values (stat_ydensity).
Warning in max(data$density): no non-missing arguments to max; returning -
Inf
Warning: Removed 4571 rows containing missing values (geom_point).

Some NF-1 and/or Other inherited conditions NOS have high scoring TP53 mutants as well.

Interesting 2 bs ids annotated with Li-Fraumeni synfrome pre-disposition have very low tp53 classifier scores.

tp53_alterations_score_wide %>%
  filter(cancer_predispositions == "Li-Fraumeni syndrome",
         tp53_score < 0.5)

Save file

Adding DNA and RNA biospecimen ids to tp53_alterations_score_wide and saving

tp53_alterations_score_wide %>% 
  write_tsv(file.path(results_dir,"tp53_altered_status.tsv"))
---
title: "Tp53 SNV hotspots"
author: "K S Gaonkar (D3B)"
output: html_notebook
params:
  base_run:
    label: "1/0 to run with base histology"
    value: 0
    input: integer
---

In this notebook we will add TP53 alteration status as discussed in [#837](https://github.com/AlexsLemonade/OpenPBTA-analysis/issues/837):

**TP53 altered - loss**, if :

 - A sample contains a TP53 hotspot SNV mutation. (Cancer hotspot database and downloadable file available). Please also crosscheck that all mutations from this table are included.
 - A sample contains two TP53 alterations, suggesting (but not confirming) that both alleles are affected (SNV+SNV, (CNV or SV) + SNV).
 - A sample contains one alteration (SNV or (CNV or SV)) + has cancer_predispositions == "Li-Fraumeni syndrome", suggesting there is a germline variant in addition to the somatic variant we observe.
 - A sample does not have a TP53 alterations, but has cancer_predispositions == "Li-Fraumeni syndrome" and TP53 classifier score for matched RNA-Seq > 0.5 (or higher cutoff we decide upon later).
 
 Note: CNV and SV will be considered as the same event, but we will be adding SV_counts and SV.type to the altered status output file
 
**TP53 altered - activated**, if:

- A sample contains one of the two TP53 activating mutations p.R273C and p.R248W. [@doi:10.1038/ng0593-42]

## Setup
```{r}
library("ggpubr")
library("ggthemes")
library("tidyverse")
library("ggfortify")
library("broom")

# rootdir
root_dir <- rprojroot::find_root(rprojroot::has_dir(".git"))
data_dir <- file.path(root_dir, "data")

input_dir <- file.path(root_dir,
                         "analyses",
                         "tp53_nf1_score",
                         "input")
# cell composition
cell_line_composition <- read_tsv(file.path("..",
                                            "molecular-subtyping-HGG",
                                            "input",
                                            "cell-line-composition.tsv"),
                                  col_types = 
                                    readr::cols( aliquot_id  = readr::col_character())) 

# histology
if ( params$base_run ==0 ){
  clinical<-read.delim(file.path(data_dir,"histologies.tsv"), stringsAsFactors = FALSE)
} else{
  clinical<-read.delim(file.path(data_dir,"histologies-base.tsv"), stringsAsFactors = FALSE)  
}

histology <- clinical %>%
   select("Kids_First_Biospecimen_ID",
          "sample_id",
          "cancer_predispositions",
          "sample_type",
          "experimental_strategy",
          "composition") %>%
   # merge cell line composition
  left_join(cell_line_composition)
 

# Cancer database
hotspot_database_2017_tp53_snv <- readxl::read_xls(file.path(input_dir,"hotspots_v2.xls"),sheet = 1) %>%
  filter(Hugo_Symbol == "TP53")
hotspot_database_2017_tp53_indel <- readxl::read_xls(file.path(input_dir,"hotspots_v2.xls"),sheet = 2) %>%
  filter(Hugo_Symbol == "TP53")

# p53, p63 and p73 functional sites
functional_sites_tp53 <- read_tsv(file.path(input_dir,"hotspot_Chen_2006.tsv")) 

results_dir <- file.path(root_dir,
                         "analyses",
                         "tp53_nf1_score",
                         "results")

if (!dir.exists(results_dir)) {
  dir.create(results_dir)
}

# classifier scores
classifier_score <- read_tsv(
  file.path(
    results_dir,
    "gene-expression-rsem-tpm-collapsed_classifier_scores.tsv"))%>%
  select(sample_id,tp53_score) %>%
  dplyr::rename(Kids_First_Biospecimen_ID_RNA=sample_id) %>%
  left_join(histology ,by=c("Kids_First_Biospecimen_ID_RNA"="Kids_First_Biospecimen_ID")) 

# Copy number per sample 
# overlapping functional domain
cnv_domain_overlap <- read_tsv(
  file.path(
    results_dir,
    "loss_overlap_domains_tp53.tsv"))

# Structural variant overlapping 
# or within gene locus of TP53
sv_overlap <- read_tsv(
  file.path(
    results_dir,
    "sv_overlap_tp53.tsv"))

# Structural variant as a fusion in exon1/intron1
fusion_overlap <- read_tsv(
  file.path(
    results_dir,
    "fusion_bk_tp53_loss.tsv"))

```

### Check if all function sites are in hotspots

All functional sites are just 1 base so checking in `hotspot_database_2017_snv`

```{r}

functional_sites_tp53 %>%
  filter(!gsub("[A-Z|a-z]","",p53) %in% hotspot_database_2017_tp53_snv$Amino_Acid_Position )

```
2 functional sites are missing in hotspots database.

## SNV for TP53

Removing Silent or Intron classified variants to capture only putative damaging mutations

```{r}
consensus_tp53_snv_indel <- data.table::fread(
  file.path(data_dir,"snv-consensus-plus-hotspots.maf.tsv.gz"),
                                   select = c("Chromosome",
                                              "Start_Position",
                                              "End_Position",
                                              "Strand",
                                              "Variant_Classification",
                                              "Tumor_Sample_Barcode",
                                              "Hugo_Symbol",
                                              "HGVSp_Short"),
                                   data.table = FALSE) %>%
  filter(Hugo_Symbol == "TP53") %>%
  filter(!(Variant_Classification %in% c("Silent", "Intron",
                                         # remove other non-amino acid SNVs
                                         "3'Flank" ,"5'Flank",
                                         "3'UTR", "5'UTR"  )))
```

###  Gather annotation for SNV 

hotspots : overlaps AA position which are statistically significant SNV/Indels
activating : overlaps AA position which are found to act as gain-of-function according to literature

```{r}

consensus_tp53_snv_indel <- consensus_tp53_snv_indel %>%
  mutate(
    hotspot = case_when(
      # strip REF and Variant AA to get Amino_Acid_Position in consensus maf
      (gsub("[A-Z|a-z]|[.]","",HGVSp_Short) %in% 
         # if overlaps the hotspot Amino_Acid_Position 
         hotspot_database_2017_tp53_snv$Amino_Acid_Position)  ~ 1,
      TRUE ~ 0),
    activating = case_when(
      # strip REF and Variant AA to get Amino_Acid_Position in consensus maf
      (gsub("[A-Z|a-z]|[.]","",HGVSp_Short) %in% 
         # if overlaps the activating  Amino_Acid_Position 
         c("273","248"))  ~ 1,
      TRUE ~ 0
    )
  )

```

## Gather SNV,CNV, classifier and cancer_predisposition values

We will gather values per sample_id since DNA and RNA samples can be only matched by `sample_id`


```{r}

tp53_alterations <- histology %>%
  filter(experimental_strategy != "RNA-Seq") %>%
  # merge cell line composition
  left_join(cell_line_composition) %>%
  # filter for Tumors
  filter(sample_type=="Tumor") %>%
  # join consensus calls overlapping hotspots
  left_join(consensus_tp53_snv_indel, 
            by=c("Kids_First_Biospecimen_ID"="Tumor_Sample_Barcode")) %>%
  # join filtered cnv losses
  left_join(cnv_domain_overlap,by=c("Kids_First_Biospecimen_ID"="biospecimen_id",
                                    "sample_id")) %>%
  # join SV 
  left_join(sv_overlap,by=c("Kids_First_Biospecimen_ID"="Kids.First.Biospecimen.ID.Tumor",
                            "sample_id")) %>%
  dplyr::rename(Kids_First_Biospecimen_ID_DNA = Kids_First_Biospecimen_ID) %>%
  # join fusion
  left_join(fusion_overlap,by="sample_id") 

tp53_alterations_score <- tp53_alterations %>%
  # add classifier score
  full_join(classifier_score,by=c("sample_id",
                                  "cell_line_composition",
                                  "cancer_predispositions",
                                  "sample_type",
                                  "Kids_First_Participant_ID")) %>%
  # select useful columns
  select(sample_id,Kids_First_Biospecimen_ID_RNA,Kids_First_Biospecimen_ID_DNA,
         tp53_score,cancer_predispositions,HGVSp_Short,copy_number,SV.type,FusionName,hotspot,activating) %>%
  arrange(sample_id) %>%
  unique() %>%
  replace_na(list(hotspot = 0, activating = 0))

tp53_alterations_score 

```

Convert the above to wide format

```{r}

tp53_alterations_score_wide <- tp53_alterations_score %>%
  # group 
  group_by(sample_id,
           Kids_First_Biospecimen_ID_DNA,
           Kids_First_Biospecimen_ID_RNA,
           cancer_predispositions,
           tp53_score) %>%
  # 
  # sample_id as rows add SNV counts,CNV loss counts 
  # and hotspot/activating mutation annotation  
  summarise(
    # summarize length of SNV and CNV alterations 
   SNV_indel_counts= length(unique(HGVSp_Short[!is.na(HGVSp_Short)])), 
    CNV_loss_counts = length(unique(copy_number[!is.na(copy_number)])),
    SV_counts = length(unique(SV.type[!is.na(SV.type)])),
    Fusion_counts = length(unique(FusionName[!is.na(FusionName)])),
    HGVSp_Short = toString(unique(HGVSp_Short)),
    CNV_loss_evidence = toString(unique(copy_number)),
    SV_type = toString(unique(SV.type)),
    Fusion_evidence = toString(unique(FusionName)),
    # summarize unique hotspot values per sample_id
    hotspot = max(unique(hotspot[!is.na(hotspot) ])),
    activating = max(unique(activating[!is.na(activating)]))) 

tp53_alterations_score_wide

```

### Add annotation

As discussed above we want to annotate TP53 mutants that are putative loss-of-function OR  gain-of-function.

```{r}

tp53_alterations_score_wide <- tp53_alterations_score_wide %>%
  mutate(
    # add tp53_altered annotation column
    tp53_altered = 
      case_when(
        # when activating == 0
        activating == 0 &
          # check if mutated variant AA position overlaps hotspot database 
          ( hotspot == 1  |
              # check if sample_id has SNV+(CNV|SV) mutation
              # suggesting both alleles are mutated
              (SNV_indel_counts >= 1 & 
                 (CNV_loss_counts >=1 | SV_counts >=1) ) |
              # check if more than 1 SNV is present
              # suggesting both alleles are mutated
              SNV_indel_counts > 1 |
              # check if SNV mutant and has
              # Li-Fraumeni syndrome suggesting
              # germline TP53 mutant as cancer predisposition 
              (SNV_indel_counts >= 1 & 
                             grepl("Li-Fraumeni syndrome",cancer_predispositions)) |
              # check if CNV|SV mutant  and has
              # Li-Fraumeni syndrome suggesting
              # germline TP53 mutant as cancer predisposition 
              ((CNV_loss_counts >= 1 | SV_counts >=1 ) & 
                             grepl("Li-Fraumeni syndrome",cancer_predispositions)) |
              # check if tp53 inactivating score for RNA_Seq is greater that 0.5
              # and has Li-Fraumeni syndrome suggesting
              # germline TP53 mutant as cancer predisposition 
              (tp53_score > 0.5 & 
                             grepl("Li-Fraumeni syndrome",cancer_predispositions)) |
              # check if tp53 inactivating score for RNA_Seq is greater that 0.5
              # and has 1 or more SNV| (CNV|SV) loss in TP53
              (tp53_score > 0.5 & 
                             (SNV_indel_counts >= 1 | (CNV_loss_counts >=1 | SV_counts >=1 )))
          ) ~ "loss",
        # when activating == 1
        activating == 1 ~ "activated",
        # if no evidence supports TP53 inativation or activation 
        TRUE ~ "Other"
      )
  )

```

## Explore distribution of tp53_altered status vs tp53 inactivation scores

```{r, out.width="50%"}
ggplot(tp53_alterations_score_wide, aes(x = factor(tp53_altered), y = tp53_score)) +
  geom_violin()+
  geom_jitter(alpha = 0.5, width = 0.2) +
  stat_compare_means() +
  theme_bw() +
  ggtitle("Distribution of scores across tp53 altered status") +
  theme(axis.text.x = element_text(angle = 60, hjust = 1))+
  xlab("tp53 altered status") 

```
TP53 mutants with gain-of-function (activating) mutants promote tumorigenesis according to literature. [Reference](https://pubmed.ncbi.nlm.nih.gov/17417627/) and [reference](https://pubmed.ncbi.nlm.nih.gov/24677579/) have similar distribution of classifier scores to that of potential loss-of-function (multi-allelic) Tp53 mutations. 

"Other" annotated samples either don't have any high-confidence loss/gain TP53 SNVs nor CNV losses OR DNA sample is not available.


### Expression profile for activating vs loss TP53 status
```{r}

expr_combined <- readRDS(file.path(data_dir,"gene-counts-rsem-expected_count-collapsed.rds"))

```

#### Expression profile for activating vs loss TP53 status

```{r, out.width="50%"}
# iterate through all possible RNA library type 
# first filter to 
clinical_filtered <- clinical %>% 
  filter(sample_type == "Tumor",
         experimental_strategy == "RNA-Seq") %>%
  filter(cohort =="PBTA") %>%
  filter(!is.na(RNA_library))

# generate list of all RNA library type
rna_library_list <- clinical_filtered %>% 
  pull(RNA_library) %>% 
  unique()

for(i in 1:length(rna_library_list)) {
  
  library_each <- rna_library_list[[i]]
  # pull BS IDs for that RNA library type
  library_samples <- clinical_filtered %>% 
    filter(RNA_library == library_each) %>%
    pull(Kids_First_Biospecimen_ID) %>% 
    unique()
  # subset expression matrix using that
  expr_each <- expr_combined %>% select(library_samples)

  # subset to TP53
  subset_expr_each <- t(expr_each)[,"TP53"] %>% 
    as.data.frame() 
  colnames(subset_expr_each) <- "TP53"
  
  # combine the TP53 alteration information
  subset_expr_combined <- subset_expr_each %>%
    tibble::rownames_to_column() %>% 
    left_join(tp53_alterations_score_wide,by=c("rowname"="Kids_First_Biospecimen_ID_RNA")) %>%
    filter(tp53_altered %in% c("activated","loss"))
  
  # plot distribution of TP53 gene expression 
  plot<- ggplot(subset_expr_combined, aes(x = factor(tp53_altered), y = TP53)) +
    geom_violin()+
    geom_jitter(alpha = 0.5, width = 0.2) +
    stat_compare_means() +
    theme_bw() +
    ggtitle("Distribution of TP53 expression across tp53 altered status") +
    theme(axis.text.x = element_text(angle = 60, hjust = 1))+
    xlab(paste0("tp53 altered status wth RNA library ", library_each))

  print(plot)
}


```


### Check if other cancer predisposition have high TP53 inactivation scores  

```{r ,out.width="50%"}

ggplot(tp53_alterations_score_wide, aes(x = factor(tp53_altered), y = tp53_score)) +
  geom_violin()+
  geom_jitter(alpha = 0.5, width = 0.2) +
  theme_bw() +
  ggtitle("Distribution of scores across tp53 altered status") +
  theme(axis.text.x = element_text(angle = 60, hjust = 1))+
  xlab("tp53 altered status") +
  facet_wrap(.~cancer_predispositions)

```
Some NF-1 and/or Other inherited conditions NOS have high scoring TP53 mutants as well. 

Interesting 2 bs ids annotated with Li-Fraumeni synfrome pre-disposition have very low tp53 classifier scores.

```{r}

tp53_alterations_score_wide %>%
  filter(cancer_predispositions == "Li-Fraumeni syndrome",
         tp53_score < 0.5) 
  
```


## Save file 

Adding DNA and RNA biospecimen ids to `tp53_alterations_score_wide` and saving

```{r}

tp53_alterations_score_wide %>% 
  write_tsv(file.path(results_dir,"tp53_altered_status.tsv"))

```
